Motion-based analysis for construction workers using biomechanical methods

Xincong YANG , Yantao YU , Heng LI , Xiaochun LUO , Fenglai WANG

Front. Eng ›› 2017, Vol. 4 ›› Issue (1) : 84 -91.

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Front. Eng ›› 2017, Vol. 4 ›› Issue (1) : 84 -91. DOI: 10.15302/J-FEM-2017004
RESEARCH ARTICLE
RESEARCH ARTICLE

Motion-based analysis for construction workers using biomechanical methods

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Abstract

Sustaining awkward postures and overexertion are common factors in construction industry that result in work-related injuries of workers. To address there safety and health issues, conventional observational methods on the external causes are tedious and subjective, while the direct measurement on the internal causes is intrusive leading to productivity reduction. Therefore, it is essential to construct an effective approach that maps the external and internal causes to realize the non-intrusive identification of safety and health risks. This research proposes a theoretical method to analyze the postures tracked by videos with biomechanical models. Through the biomechanical skeleton representation of human body, the workload and joint torques are rapidly and accurately evaluated based on the rotation angles of joints. The method is then demonstrated by two case studies about (1) plastering and (2) carrying. The experiment results illustrate the changing intramuscular torques across the construction activities in essence, validating the proposed approach to be effective in theory.

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Keywords

biomechanical method / motion-based analysis / construction worker / muscular torques / workload

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Xincong YANG, Yantao YU, Heng LI, Xiaochun LUO, Fenglai WANG. Motion-based analysis for construction workers using biomechanical methods. Front. Eng, 2017, 4(1): 84-91 DOI:10.15302/J-FEM-2017004

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References

[1]

Buchholz B, Paquet V, Punnett L, Lee D, Moir S (1996). PATH: A work sampling-based approach to ergonomic job analysis for construction and other non-repetitive work. Applied Ergonomics, 27(3): 177–187

[2]

Faber A, Strøyer J, Hjortskov N, Schibye B (2010). Changes in physical performance among construction workers during extended workweeks with 12-hour workdays. International Archives of Occupational and Environmental Health, 83(1): 1–8

[3]

Garet M, Boudet G, Montaurier C, Vermorel M, Coudert J, Chamoux A (2005). Estimating relative physical workload using heart rate monitoring: A validation by whole-body indirect calorimetry. European Journal of Applied Physiology, 94(1–2): 46–53

[4]

Gatti U C, Migliaccio G C, Schneider S (2011). Wearable physiological status monitors for measuring and evaluating workers’ physical strain: Preliminary validation. In: Proceedings of International Workshop on Computing in Civil Engineering. Miami: American Society of Civil Engineers, 194–201

[5]

Golabchi A, Han S, Fayek A R (2016). A fuzzy logic approach to posture-based ergonomic analysis for field observation and assessment of construction manual operations. Canadian Journal of Civil Engineering, 43(4): 294–303

[6]

Golabchi A, Han S, Seo J, Han S U, Lee S H, Al-Hussein M (2015). An automated biomechanical simulation approach to ergonomic job analysis for workplace design. Journal of Construction Engineering and Management, 141(8): 04015020

[7]

Gong J, Caldas C H (2011). Learning and classifying motions of construction workers and equipment using bag of video feature words and Bayesian learning methods. In: Proceedings of International Workshop on Computing in Civil Engineering. Reston: American Society of Civil Engineers, 274–281

[8]

Hartmann B, Fleischer A G (2005). Physical load exposure at construction sites. Scandinavian Journal of Work, Environment & Health, 31(Suppl 2): 88–95

[9]

Han S, Lee S, Feniosky P M (2014). Comparative study of motion features for similarity-based modeling and classification of unsafe actions in construction. Journal of Computing in Civil Engineering, 28(5): A4014005

[10]

Kinovea (2017). A microscope for your videos.

[11]

Liu M, Han S, Lee S (2016). Tracking-based 3D human skeleton extraction from stereo video camera toward an on-site safety and ergonomic analysis. Construction Innovation, 16(3): 348–367

[12]

Martínez-Rojas M, Marín N, Vila M A (2016). The role of information technologies to address data handling in construction project management. Journal of Computing in Civil Engineering, 30(4): 1–10

[13]

Occupational Safety and Health Branch Labour Department. (2016). Occupational Safety and Health Statistics Statistics 2015.

[14]

Peddi A, Huan L, Bai Y, Kim S (2009). Development of human pose analyzing algorithms for the determination of construction productivity in real-time . In: Proceedings of Construction Research Congress. Reston: American Society of Civil Engineers, 11–20

[15]

Pinto A, Nunes I L, Ribeiro R A (2011). Occupational risk assessment in construction industry—Overview and reflection. Safety Science, 49(5): 619–624

[16]

Plagenhoef S, Evans F G, Abdelnour T (1983). Anatomical data for analyzing human motion. Research Quarterly for Exercise and Sport, 54(2): 169–178

[17]

Ray S J, Teizer J (2012). Real-time construction worker posture analysis for ergonomics training. Advanced Engineering Informatics, 26(2): 439–455

[18]

Takala A E, Pehkonen I, Forsman M, Hansson G Å, Mathiassen S E, Neumann W P, Sjøgaard G, Veiersted K B, Westgaard R H, Winkel J (2010). Systematic evaluation of observational methods assessing biomechanical exposures at work. Scandinavian Journal of Work, Environment & Health, 36(1): 3–24

[19]

Umer W, Li H, Szeto G P Y, Wong A Y L (2017). Identification of biomechanical risk factors for the development of low back disorders during manual rebar tying. Journal of Construction Engineering and Management, 143(1): 1–10

[20]

video4Coach (2017). SkillCapture.

[21]

Yan X, Li H, Li A R, Zhang H (2017). Wearable IMU-based real-time motion warning system for construction workers’ musculoskeletal disorders prevention. Automation in Construction, 74: 2–11

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The Author (s) 2017. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)

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